{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python绘制热力图查看两个分类变量之间的强度分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 热力图(heat map)\n",
    "\n",
    "热力图（或者色块图），由小色块组成的二维图表，其中：\n",
    "* x、y轴可以是分类变量，对应的小方块由连续数值表示颜色强度\n",
    "* 即用两个分类字段确定数值点的位置，用于展示数据的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "sns.set_style('whitegrid', {'font.sans-serif':['simhei', 'Arial']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实例1：模拟绘制北京景区热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>周一</th>\n",
       "      <th>周二</th>\n",
       "      <th>周三</th>\n",
       "      <th>周四</th>\n",
       "      <th>周五</th>\n",
       "      <th>周六</th>\n",
       "      <th>周日</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>天安门</th>\n",
       "      <td>0.074419</td>\n",
       "      <td>0.214726</td>\n",
       "      <td>0.999661</td>\n",
       "      <td>0.665986</td>\n",
       "      <td>0.677667</td>\n",
       "      <td>0.197178</td>\n",
       "      <td>0.608004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>故宫</th>\n",
       "      <td>0.892415</td>\n",
       "      <td>0.944866</td>\n",
       "      <td>0.506391</td>\n",
       "      <td>0.974252</td>\n",
       "      <td>0.371753</td>\n",
       "      <td>0.683809</td>\n",
       "      <td>0.715924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>奥林匹克森林公园</th>\n",
       "      <td>0.659481</td>\n",
       "      <td>0.790725</td>\n",
       "      <td>0.266958</td>\n",
       "      <td>0.641157</td>\n",
       "      <td>0.726409</td>\n",
       "      <td>0.746208</td>\n",
       "      <td>0.213861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>八达岭长城</th>\n",
       "      <td>0.891930</td>\n",
       "      <td>0.305052</td>\n",
       "      <td>0.054287</td>\n",
       "      <td>0.635466</td>\n",
       "      <td>0.890898</td>\n",
       "      <td>0.259909</td>\n",
       "      <td>0.567301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                周一        周二        周三        周四        周五        周六        周日\n",
       "天安门       0.074419  0.214726  0.999661  0.665986  0.677667  0.197178  0.608004\n",
       "故宫        0.892415  0.944866  0.506391  0.974252  0.371753  0.683809  0.715924\n",
       "奥林匹克森林公园  0.659481  0.790725  0.266958  0.641157  0.726409  0.746208  0.213861\n",
       "八达岭长城     0.891930  0.305052  0.054287  0.635466  0.890898  0.259909  0.567301"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    np.random.rand(4, 7), \n",
    "    index = [\"天安门\", \"故宫\", \"奥林匹克森林公园\", \"八达岭长城\"],\n",
    "    columns = [\"周一\", \"周二\", \"周三\", \"周四\", \"周五\", \"周六\", \"周日\"]\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d857b0e988>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "sns.heatmap(df, annot=True, fmt = \".2f\", cmap = \"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实例2：绘制泰坦尼克事件与存亡变量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取并合并泰坦尼克数据\n",
    "df = pd.concat(\n",
    "    [\n",
    "        pd.read_csv(\"./datas/titanic/titanic_train.csv\"),\n",
    "        pd.read_csv(\"./datas/titanic/titanic_test.csv\")\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        295 non-null    object \n",
      " 11  Embarked     1307 non-null   object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pands把字符串类型的列，变成分类数字编码\n",
    "for field in [\"Sex\", \"Cabin\", \"Embarked\"]:\n",
    "    df[field] = df[field].astype(\"category\").cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   int8   \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        1309 non-null   int16  \n",
      " 11  Embarked     1309 non-null   int8   \n",
      "dtypes: float64(3), int16(1), int64(4), int8(2), object(2)\n",
      "memory usage: 107.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "      <th>Embarked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "\n",
       "             Ticket     Fare  Cabin  Embarked  \n",
       "0         A/5 21171   7.2500     -1         2  \n",
       "1          PC 17599  71.2833    106         0  \n",
       "2  STON/O2. 3101282   7.9250     -1         2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
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       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.277885</td>\n",
       "      <td>-0.176509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.038354</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.124617</td>\n",
       "      <td>-0.408106</td>\n",
       "      <td>0.060832</td>\n",
       "      <td>0.018322</td>\n",
       "      <td>-0.558629</td>\n",
       "      <td>-0.534483</td>\n",
       "      <td>0.192867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.013406</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>0.124617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063645</td>\n",
       "      <td>-0.109609</td>\n",
       "      <td>-0.213125</td>\n",
       "      <td>-0.185523</td>\n",
       "      <td>-0.126367</td>\n",
       "      <td>0.104818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
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       "      <td>-0.077221</td>\n",
       "      <td>-0.408106</td>\n",
       "      <td>0.063645</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.243699</td>\n",
       "      <td>-0.150917</td>\n",
       "      <td>0.178740</td>\n",
       "      <td>0.190984</td>\n",
       "      <td>-0.089292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.055224</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.060832</td>\n",
       "      <td>-0.109609</td>\n",
       "      <td>-0.243699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.373587</td>\n",
       "      <td>0.160238</td>\n",
       "      <td>-0.005685</td>\n",
       "      <td>0.067802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018322</td>\n",
       "      <td>-0.213125</td>\n",
       "      <td>-0.150917</td>\n",
       "      <td>0.373587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.221539</td>\n",
       "      <td>0.029582</td>\n",
       "      <td>0.046957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.031428</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.558629</td>\n",
       "      <td>-0.185523</td>\n",
       "      <td>0.178740</td>\n",
       "      <td>0.160238</td>\n",
       "      <td>0.221539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340217</td>\n",
       "      <td>-0.241442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>-0.012096</td>\n",
       "      <td>0.277885</td>\n",
       "      <td>-0.534483</td>\n",
       "      <td>-0.126367</td>\n",
       "      <td>0.190984</td>\n",
       "      <td>-0.005685</td>\n",
       "      <td>0.029582</td>\n",
       "      <td>0.340217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.126482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>-0.048530</td>\n",
       "      <td>-0.176509</td>\n",
       "      <td>0.192867</td>\n",
       "      <td>0.104818</td>\n",
       "      <td>-0.089292</td>\n",
       "      <td>0.067802</td>\n",
       "      <td>0.046957</td>\n",
       "      <td>-0.241442</td>\n",
       "      <td>-0.126482</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Sex       Age     SibSp  \\\n",
       "PassengerId     1.000000 -0.005007 -0.038354  0.013406  0.028814 -0.055224   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.543351 -0.077221 -0.035322   \n",
       "Pclass         -0.038354 -0.338481  1.000000  0.124617 -0.408106  0.060832   \n",
       "Sex             0.013406 -0.543351  0.124617  1.000000  0.063645 -0.109609   \n",
       "Age             0.028814 -0.077221 -0.408106  0.063645  1.000000 -0.243699   \n",
       "SibSp          -0.055224 -0.035322  0.060832 -0.109609 -0.243699  1.000000   \n",
       "Parch           0.008942  0.081629  0.018322 -0.213125 -0.150917  0.373587   \n",
       "Fare            0.031428  0.257307 -0.558629 -0.185523  0.178740  0.160238   \n",
       "Cabin          -0.012096  0.277885 -0.534483 -0.126367  0.190984 -0.005685   \n",
       "Embarked       -0.048530 -0.176509  0.192867  0.104818 -0.089292  0.067802   \n",
       "\n",
       "                Parch      Fare     Cabin  Embarked  \n",
       "PassengerId  0.008942  0.031428 -0.012096 -0.048530  \n",
       "Survived     0.081629  0.257307  0.277885 -0.176509  \n",
       "Pclass       0.018322 -0.558629 -0.534483  0.192867  \n",
       "Sex         -0.213125 -0.185523 -0.126367  0.104818  \n",
       "Age         -0.150917  0.178740  0.190984 -0.089292  \n",
       "SibSp        0.373587  0.160238 -0.005685  0.067802  \n",
       "Parch        1.000000  0.221539  0.029582  0.046957  \n",
       "Fare         0.221539  1.000000  0.340217 -0.241442  \n",
       "Cabin        0.029582  0.340217  1.000000 -0.126482  \n",
       "Embarked     0.046957 -0.241442 -0.126482  1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算不同变量之间两两相关系数\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "d:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d85d235c88>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(df.corr(), annot=True, fmt = \".2f\", cmap = \"coolwarm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
